By Masao Sakauchi (auth.), Hiroshi Arisawa, Tiziana Catarci (eds.)
Video segmentation is the main basic approach for applicable index ing and retrieval of video durations. usually, video streams are composed 1 of pictures delimited by way of actual shot limitations. vast paintings has been performed on tips to notice such shot limitations instantly (Arman et aI. , 1993) (Zhang et aI. , 1993) (Zhang et aI. , 1995) (Kobla et aI. , 1997). in the course of the inte gration of applied sciences akin to snapshot processing, speech/character attractiveness and ordinary language figuring out, key terms may be extracted and linked to those pictures for indexing (Wactlar et aI. , 1996). A unmarried shot, although, infrequently incorporates adequate quantity of knowledge to be significant on its own. Usu best friend, it's a semantically significant period that almost all clients have an interest in re trieving. as a rule, such significant durations span a number of consecutive photographs. There hardly ever exists any effective and trustworthy process, both computerized or handbook, to spot all semantically significant durations inside a video move. Works by way of (Smith and Davenport, 1992) (Oomoto and Tanaka, 1993) (Weiss et aI. , 1995) (Hjelsvold et aI. , 1996) recommend manually defining all such inter vals within the database upfront. notwithstanding, even an hour lengthy video can have an indefinite variety of significant periods. additionally, video information is multi interpretative. as a result, given a question, what's a significant period to an annotator will not be significant to the consumer who concerns the question. In perform, guide indexing of significant periods is labour in depth and inadequate.
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Additional resources for Advances in Visual Information Management: Visual Database Systems. IFIP TC2 WG2.6 Fifth Working Conference on Visual Database Systems May 10–12, 2000, Fukuoka, Japan
Example text
These have brought the Web technology to the desktop of any computer. Over the past two years, the Web technology enables the Java programming language to spark considerable interest among software developers. Its popularity stems from its flexibility, portability, and relative simplicity compared to other object-oriented programming languages (Jain & Schmidt 1997). The applet feature supported by Java is particularly relevant to distributed multimedia system. An applet is a Java class that can be downloaded from a Web server and run in a context application such as a Web browser or an applet viewer.
Keywords: Video Search Engine, Information Retrieval, Multimedia Database 1. INTRODUCTION Lot of works in the field of multimedia management systems have followed generic approaches. Furthermore, Visualisation tools have been developed such as video search engines by content (Ram 1999), and H. Arisawa et al. ), Advances in Visual Information Management © Springer Science+Business Media New York 2000 32 VISUAL DATABASE SYSTEMS especially for the manipulation and the storage of video features (Flickner et ai.
Supposing X is the set of all the intervals where the keyword k appears and U is the set of all the subintervals of the video data in the database. Then, for the keyword k, noise is computed as: Noise(k) = max(X) where X = {i E U I (Vi'(=/= i) E X)(i' n i = 0)} and max(Z) = {i liE Z and (Vi'(i' =/= i) E Z)(i ~ i' or in i' = 0)} For a set of keywords K = {kl' k 2, ... , kn }, 'noise' is defined as a set of those intervals in which none of keyword {kl' k2' ... , kn } appear at all. In order to compute the 'noise' for a set of keywords, we need to provide the usual definition of interval intersection operation and its set-variant.